AutoML Leaderboard

Best model	name	model_type	metric_type	metric_value	train_time
	1_Baseline	Baseline	logloss	1.06587	1.44
the best	2_DecisionTree	Decision Tree	logloss	3e-06	19.31
	3_Default_Xgboost	Xgboost	logloss	0.000156015	163.61
	4_Default_NeuralNetwork	Neural Network	logloss	0.00871767	9.53
	5_Default_RandomForest	Random Forest	logloss	3e-06	12.7
	Ensemble	Ensemble	logloss	3e-06	0.42

AutoML Performance

AutoML Performance Boxplot

Features Importance

Spearman Correlation of Models

Summary of Ensemble

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Ensemble structure

Model	Weight
2_DecisionTree	1

Metric details

	Extreme	Major	Minor	Moderate	accuracy	macro avg	weighted avg	logloss
precision	1	1	1	1	1	1	1	3e-06
recall	1	1	1	1	1	1	1	3e-06
f1-score	1	1	1	1	1	1	1	3e-06
support	220	647	2634	921	1	4422	4422	3e-06

Confusion matrix

	Predicted as Extreme	Predicted as Major	Predicted as Minor	Predicted as Moderate
Labeled as Extreme	220	0	0	0
Labeled as Major	0	647	0	0
Labeled as Minor	0	0	2634	0
Labeled as Moderate	0	0	0	921

Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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Summary of 2_DecisionTree

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Decision Tree

• n_jobs: -1
• criterion: gini
• max_depth: 3
• num_class: 4
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

18.4 seconds

Metric details

	Extreme	Major	Minor	Moderate	accuracy	macro avg	weighted avg	logloss
precision	1	1	1	1	1	1	1	3e-06
recall	1	1	1	1	1	1	1	3e-06
f1-score	1	1	1	1	1	1	1	3e-06
support	220	647	2634	921	1	4422	4422	3e-06

Confusion matrix

	Predicted as Extreme	Predicted as Major	Predicted as Minor	Predicted as Moderate
Labeled as Extreme	220	0	0	0
Labeled as Major	0	647	0	0
Labeled as Minor	0	0	2634	0
Labeled as Moderate	0	0	0	921

Learning curves

Decision Tree

Tree #1

Rules

if (APR Risk of Mortality <= 2.5) and (APR Risk of Mortality > 1.5) then class: Minor (proba: 100.0%) | based on 7,899 samples

if (APR Risk of Mortality > 2.5) then class: Moderate (proba: 100.0%) | based on 2,764 samples

if (APR Risk of Mortality <= 2.5) and (APR Risk of Mortality <= 1.5) and (APR Risk of Mortality > 0.5) then class: Major (proba: 100.0%) | based on 1,942 samples

if (APR Risk of Mortality <= 2.5) and (APR Risk of Mortality <= 1.5) and (APR Risk of Mortality <= 0.5) then class: Extreme (proba: 100.0%) | based on 658 samples

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

SHAP Importance

SHAP Dependence plots

Dependence Extreme (Fold 1)

Dependence Major (Fold 1)

Dependence Minor (Fold 1)

Dependence Moderate (Fold 1)

SHAP Decision plots

Worst decisions for selected sample 1 (Fold 1)

Worst decisions for selected sample 2 (Fold 1)

Worst decisions for selected sample 3 (Fold 1)

Worst decisions for selected sample 4 (Fold 1)

Best decisions for selected sample 1 (Fold 1)

Best decisions for selected sample 2 (Fold 1)

Best decisions for selected sample 3 (Fold 1)

Best decisions for selected sample 4 (Fold 1)

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Summary of 3_Default_Xgboost

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Extreme Gradient Boosting (Xgboost)

• n_jobs: -1
• objective: multi:softprob
• eta: 0.075
• max_depth: 6
• min_child_weight: 1
• subsample: 1.0
• colsample_bytree: 1.0
• eval_metric: mlogloss
• num_class: 4
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

162.6 seconds

Metric details

	Extreme	Major	Minor	Moderate	accuracy	macro avg	weighted avg	logloss
precision	1	1	1	1	1	1	1	0.000156015
recall	1	1	1	1	1	1	1	0.000156015
f1-score	1	1	1	1	1	1	1	0.000156015
support	220	647	2634	921	1	4422	4422	0.000156015

Confusion matrix

	Predicted as Extreme	Predicted as Major	Predicted as Minor	Predicted as Moderate
Labeled as Extreme	220	0	0	0
Labeled as Major	0	647	0	0
Labeled as Minor	0	0	2634	0
Labeled as Moderate	0	0	0	921

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

SHAP Importance

SHAP Dependence plots

Dependence Extreme (Fold 1)

Dependence Major (Fold 1)

Dependence Minor (Fold 1)

Dependence Moderate (Fold 1)

SHAP Decision plots

Worst decisions for selected sample 1 (Fold 1)

Worst decisions for selected sample 2 (Fold 1)

Worst decisions for selected sample 3 (Fold 1)

Worst decisions for selected sample 4 (Fold 1)

Best decisions for selected sample 1 (Fold 1)

Best decisions for selected sample 2 (Fold 1)

Best decisions for selected sample 3 (Fold 1)

Best decisions for selected sample 4 (Fold 1)

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Summary of 4_Default_NeuralNetwork

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Neural Network

• n_jobs: -1
• dense_1_size: 32
• dense_2_size: 16
• learning_rate: 0.05
• num_class: 4
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

8.8 seconds

Metric details

	Extreme	Major	Minor	Moderate	accuracy	macro avg	weighted avg	logloss
precision	0.986547	1	0.998862	0.998914	0.998417	0.996081	0.998427	0.00871767
recall	1	0.992272	0.99962	0.998914	0.998417	0.997702	0.998417	0.00871767
f1-score	0.993228	0.996121	0.999241	0.998914	0.998417	0.996876	0.998417	0.00871767
support	220	647	2634	921	0.998417	4422	4422	0.00871767

Confusion matrix

	Predicted as Extreme	Predicted as Major	Predicted as Minor	Predicted as Moderate
Labeled as Extreme	220	0	0	0
Labeled as Major	3	642	2	0
Labeled as Minor	0	0	2633	1
Labeled as Moderate	0	0	1	920

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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Summary of 5_Default_RandomForest

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Random Forest

• n_jobs: -1
• criterion: gini
• max_features: 0.9
• min_samples_split: 30
• max_depth: 4
• eval_metric_name: logloss
• num_class: 4
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

11.8 seconds

Metric details

	Extreme	Major	Minor	Moderate	accuracy	macro avg	weighted avg	logloss
precision	1	1	1	1	1	1	1	3e-06
recall	1	1	1	1	1	1	1	3e-06
f1-score	1	1	1	1	1	1	1	3e-06
support	220	647	2634	921	1	4422	4422	3e-06

Confusion matrix

	Predicted as Extreme	Predicted as Major	Predicted as Minor	Predicted as Moderate
Labeled as Extreme	220	0	0	0
Labeled as Major	0	647	0	0
Labeled as Minor	0	0	2634	0
Labeled as Moderate	0	0	0	921

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

SHAP Importance

SHAP Dependence plots

Dependence Extreme (Fold 1)

Dependence Major (Fold 1)

Dependence Minor (Fold 1)

Dependence Moderate (Fold 1)

SHAP Decision plots

Worst decisions for selected sample 1 (Fold 1)

Worst decisions for selected sample 2 (Fold 1)

Worst decisions for selected sample 3 (Fold 1)

Worst decisions for selected sample 4 (Fold 1)

Best decisions for selected sample 1 (Fold 1)

Best decisions for selected sample 2 (Fold 1)

Best decisions for selected sample 3 (Fold 1)

Best decisions for selected sample 4 (Fold 1)

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Summary of 1_Baseline

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Baseline Classifier (Baseline)

• n_jobs: -1
• num_class: 4
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

1.0 seconds

Metric details

	Extreme	Major	Minor	Moderate	accuracy	macro avg	weighted avg	logloss
precision	0	0	0.595658	0	0.595658	0.148915	0.354809	1.06587
recall	0	0	1	0	0.595658	0.25	0.595658	1.06587
f1-score	0	0	0.746599	0	0.595658	0.18665	0.444718	1.06587
support	220	647	2634	921	0.595658	4422	4422	1.06587

Confusion matrix

	Predicted as Extreme	Predicted as Major	Predicted as Minor	Predicted as Moderate
Labeled as Extreme	0	0	220	0
Labeled as Major	0	0	647	0
Labeled as Minor	0	0	2634	0
Labeled as Moderate	0	0	921	0

Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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